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CodeLLaMA-Linux-BugFix

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bugfix
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qlora
git-diff
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metadata
license: mit
tags:
  - codellama
  - linux
  - bugfix
  - lora
  - qlora
  - git-diff
base_model: codellama/CodeLLaMA-7b-Instruct-hf
model_type: LlamaForCausalLM
library_name: peft
pipeline_tag: text-generation

CodeLLaMA-Linux-BugFix

A fine-tuned version of CodeLLaMA-7B-Instruct, designed specifically for Linux kernel bug fixing using QLoRA (Quantized Low-Rank Adaptation). The model learns to generate Git diff patches based on buggy C code and commit messages.

🎯 Overview

This project targets automated Linux kernel bug fixing by:

  • Mining real commit data from the kernel Git history
  • Training a specialized QLoRA model on diff-style fixes
  • Generating Git patches in response to bug-prone code
  • Evaluating results using BLEU, ROUGE, and human inspection

The model achieves strong performance in generating accurate Linux kernel bug fixes, making it a valuable tool for automated code review and bug detection.

📊 Performance Results

Evaluation Metrics

✅ BLEU Score: 33.87

✅ ROUGE Scores:

  • ROUGE-1: P=0.3775, R=0.7306, F1=0.4355
  • ROUGE-2: P=0.2898, R=0.6096, F1=0.3457
  • ROUGE-L: P=0.3023, R=0.6333, F1=0.3612

These results demonstrate the model's ability to:

  • Generate syntactically correct Git diff patches
  • Maintain semantic similarity to reference fixes
  • Produce meaningful code changes that address the underlying bugs

🧠 Model Configuration

  • Base model: CodeLLaMA-7B-Instruct
  • Fine-tuning method: QLoRA with 4-bit quantization
  • Training setup:
    • LoRA r=64, alpha=16, dropout=0.1
    • Batch size: 64, LR: 2e-4, Epochs: 3
    • Mixed precision (bfloat16), gradient checkpointing
  • Hardware: Optimized for NVIDIA H200 GPUs

📊 Dataset

Custom dataset extracted from Linux kernel Git history.

Filtering Criteria

Bug-fix commits containing: fix, bug, crash, memory, null, panic, overflow, race, corruption, etc.

Structure

  • Language: C (.c, .h)
  • Context: 10 lines before/after the change
  • Format:
{
  "input": {
    "original code": "C code snippet with bug",
    "instruction": "Commit message or fix description"
  },
  "output": {
    "diff codes": "Git diff showing the fix"
  }
}
  • File: training_data_100k.jsonl (100,000 samples)

🚀 Quick Start

Prerequisites

  • Python 3.8+
  • CUDA-compatible GPU (recommended)
  • 16GB+ RAM
  • 50GB+ disk space

Install dependencies 

pip install -r requirements.txt

1. Build the Dataset 

cd dataset_builder
python extract_linux_bugfixes_parallel.py
python format_for_training.py

2. Fine-tune the Model 

cd train
python train_codellama_qlora_linux_bugfix.py

3. Run Evaluation 

cd evaluate
python evaluate_linux_bugfix_model.py

4. Use the Model 

from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

# Load the fine-tuned model
model = AutoModelForCausalLM.from_pretrained("codellama/CodeLLaMA-7b-Instruct-hf")
model = PeftModel.from_pretrained(model, "train/output/qlora-codellama-bugfix")
tokenizer = AutoTokenizer.from_pretrained("codellama/CodeLLaMA-7b-Instruct-hf")

# Generate a bug fix
prompt = """
Given the following original C code:
```c
if (!file->filter)
    return;

Instruction: Fix the null pointer dereference

Return the diff that fixes it: """

inputs = tokenizer(prompt, return_tensors="pt") outputs = model.generate(**inputs, max_length=512, temperature=0.1) fix = tokenizer.decode(outputs[0], skip_special_tokens=True) print(fix)


---

## 📁 Project Structure

CodeLLaMA-Linux-BugFix/ ├── dataset_builder/ │ ├── extract_linux_bugfixes_parallel.py # Parallel extraction of bug fixes │ ├── format_for_training.py # Format data for training │ └── build_dataset.py # Main dataset builder ├── dataset/ │ ├── training_data_100k.jsonl # 100K training samples │ └── training_data_prompt_completion.jsonl # Formatted training data ├── train/ │ ├── train_codellama_qlora_linux_bugfix.py # Main training script │ ├── train_codellama_qlora_simple.py # Simplified training │ ├── download_codellama_model.py # Model download utility │ └── output/ │ └── qlora-codellama-bugfix/ # Trained model checkpoints ├── evaluate/ │ ├── evaluate_linux_bugfix_model.py # Evaluation script │ ├── test_samples.jsonl # Test dataset │ └── output/ # Evaluation results │ ├── eval_results.csv # Detailed results │ └── eval_results.json # JSON format results ├── requirements.txt # Python dependencies ├── README.md # This file └── PROJECT_STRUCTURE.md # Detailed project overview


---

## 🧩 Features

* 🔧 **Efficient Fine-tuning**: QLoRA + 4-bit quant = massive memory savings
* 🧠 **Real-world commits**: From actual Linux kernel development
* 💡 **Context-aware**: Code context extraction around bug lines
* 💻 **Output-ready**: Generates valid Git-style diffs
* 📈 **Strong Performance**: BLEU score of 33.87 with good ROUGE metrics
* 🚀 **Production-ready**: Optimized for real-world deployment

---

## 📈 Evaluation Metrics

* **BLEU**: Translation-style match to reference diffs
* **ROUGE**: Overlap in fix content and semantic similarity
* **Human Evaluation**: Subjective patch quality assessment

### Current Performance
- **BLEU Score**: 33.87 (excellent for code generation tasks)
- **ROUGE-1 F1**: 0.4355 (good semantic overlap)
- **ROUGE-2 F1**: 0.3457 (reasonable bigram matching)
- **ROUGE-L F1**: 0.3612 (good longest common subsequence)

---

## 🧪 Use Cases

* **Automated kernel bug fixing**: Generate fixes for common kernel bugs
* **Code review assistance**: Help reviewers identify potential issues
* **Teaching/debugging kernel code**: Educational tool for kernel development
* **Research in automated program repair (APR)**: Academic research applications
* **CI/CD integration**: Automated testing and fixing in development pipelines

---

## 🔬 Technical Highlights

### Memory & Speed Optimizations

* 4-bit quantization (NF4)
* Gradient checkpointing
* Mixed precision (bfloat16)
* Gradient accumulation
* LoRA parameter efficiency

### Training Efficiency

* **QLoRA**: Reduces memory usage by ~75%
* **4-bit quantization**: Further memory optimization
* **Gradient checkpointing**: Trades compute for memory
* **Mixed precision**: Faster training with maintained accuracy

---

## 🛠️ Advanced Usage

### Custom Training

```bash
# Train with custom parameters
python train_codellama_qlora_linux_bugfix.py \
    --learning_rate 1e-4 \
    --num_epochs 5 \
    --batch_size 32 \
    --lora_r 32 \
    --lora_alpha 16

Evaluation on Custom Data 

# Evaluate on your own test set
python evaluate_linux_bugfix_model.py \
    --test_file your_test_data.jsonl \
    --output_dir custom_eval_results

🤝 Contributing

  1. Fork this repo
  2. Create a feature branch (git checkout -b feature/amazing-feature)
  3. Commit your changes (git commit -m 'Add amazing feature')
  4. Push to the branch (git push origin feature/amazing-feature)
  5. Open a Pull Request 🙌

Development Guidelines

  • Follow PEP 8 style guidelines
  • Add tests for new features
  • Update documentation for API changes
  • Ensure all tests pass before submitting PR

📄 License

MIT License – see LICENSE file for details.

🙏 Acknowledgments

  • Meta for CodeLLaMA base model
  • Hugging Face for Transformers + PEFT libraries
  • The Linux kernel community for open access to commit data
  • Microsoft for introducing LoRA technique
  • University of Washington for QLoRA research

📚 References

📞 Support

For questions, issues, or contributions:

  • Open an issue on GitHub
  • Check the project documentation
  • Review the evaluation results in evaluate/output/

🔄 Version History

  • v1.0.0: Initial release with QLoRA training
  • v1.1.0: Added parallel dataset extraction
  • v1.2.0: Improved evaluation metrics and documentation